Pre-Process & Visualize Data with Tidy Techniques in R

With 39 lectures, this course will tackle the most fundamental building block of practical data science—data wrangling and visualization. It will take you from a basic level of performing some of the most common data wrangling tasks in R with two of the most important R data science packages, Tidyverse and Dplyr. It will introduce you to some of the most important data visualization concepts and techniques that will suit and apply to your data.

Read-in data into the R environment from different sources

Learn how to use some of the most important R data wrangling & visualization packages such as Dpylr and Ggplot2

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Practical Data Pre-Processing & Visualization Training with Python

This 5-hour course is created to take you by hand and teach you how to tackle the most fundamental building blocks of practical data science: data wrangling and visualization. It will equip you to use some of the most important Python data wrangling and visualization packages such as Seaborn. You will also be able to decide which wrangling and visualization techniques are best suited to answer your research questions and applicable to your data and interpret the results.

Access 49 lectures & 5 hours of content 24/7

Understand the most fundamental building blocks of practical data science

Be equipped w/ some of the most important Python data wrangling & visualization packages

Implement different techniques on real-life data

Learn a new concept or technique after each video

Note: Software not included

Instructor

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Working with Classes: Classify & Cluster Data With Python

Harness The Power of Machine Learning For Unsupervised & Supervised Learning In Python

In this course, you’ll start by absorbing the most valuable Python Data Science basics and techniques. You'll get up to speed with packages like Numpy, Pandas, and Matplotlib and work with real data in Python. You'll even delve into concepts like unsupervised learning, dimension reduction, and supervised learning.

Access 46 lectures & 4 hours of content 24/7

Harness the power of Anaconda/iPython for practical data science

Carry out basic data pre-processing & wrangling in Python

Implement dimensional reduction techniques (PCA) & feature selection

Explore neural network & deep learning-based classification

Note: Software not included

Instructor

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

This course offers a complete guide to practical data science using Python. You'll cover all aspects of practical data science in Python. By storing, filtering, managing, and manipulating data in Python, you can give your company a competitive edge and boost your career to the next level.

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Important Details

Length of time users can access this course: lifetime

Access options: web & mobile streaming

Certification of completion included

Redemption deadline: redeem your code within 30 days of purchase

Updates included

Experience level required: all levels

Requirements

PC or Mac

Internet access required

Course Outline

Introduction to the Data Science in Python Bootcamp

Welcome to the Course - 1:40

Data and Scripts for the Course

Introduction to the Python Data Science Tool - 10:57

For Mac Users - 4:05

Introduction to the Python Data Science Environment - 19:15

Some Miscellaneous IPython Usage Facts - 5:25

Online iPython Interpreter - 3:26

Conclusion to Section 1 - 2:36

Introduction to Pandas

What are Pandas? - 12:06

Read CSV Data in Python - 5:42

Read in Excel File - 5:31

Read HTML Data - 12:06

Read JSON Data - 9:14

Conclusions to Section 4 - 2:06

Data Pre-Processing/Wrangling

Remove NA Values - 10:28

Basic Data Handling: Starting with Conditional Data Selection - 5:24

Basic Data Grouping Based on Qualitative Attributes - 9:47

Rank and Sort Data - 8:03

Concatenate - 8:16

Merge - 10:47

Basic Statistical Data Analysis

What is Statistical Data Analysis? - 10:08

Some Pointers on Collecting Data for Statistical Studies - 8:38

Explore the Quantitative Data: Descriptive Statistics - 9:05

Group By Qualitative Categories - 10:25

Visualize Descriptive Statistics-Boxplots - 5:28

Common Terms Relating to Descriptive Statistics - 5:15

Data Distribution- Normal Distribution - 4:07

Check for Normal Distribution - 6:23

Standard Normal Distribution and Z-scores - 4:10

Confidence Interval-Theory - 6:06

Confidence Interval-Calculation - 5:20

Regression Modelling for Defining Relationship bw Variables

Explore the Relationship Between Two Quantitative Variables - 4:26

Correlation Analysis - 8:26

Linear Regression-Theory - 10:44

Linear Regression-Implementation in Python - 11:18

Conditions of Linear Regression-Check in Python - 12:03

Polynomial Regression - 3:53

GLM: Generalized Linear Model - 5:25

Logistic Regression - 11:10

Machine Learning for Data Science

How is Machine Learning Different from Statistical Data Analysis? - 5:36

What is Machine Learning (ML) About? Some Theoretical Pointers - 5:32

Machine Learning Based Regression Modelling

What is this section about - 10:10

Data Preparation for Supervised Learning - 9:47

Pointers on Evaluating the Accuracy of Classification and Regression Modelling - 9:42

Tensorflow & Keras Masterclass for Machine Learning and AI in Python

Master the Most Important Deep Learning Frameworks for Python Data Science

This course is your complete guide to the practical machine and deep learning using the Tensorflow and Keras frameworks in Python. In the age of Big Data, companies across the globe use Python to sift through the avalanche of information at their disposal and the advent of Tensorflow and Keras is revolutionizing deep learning. This course will help you break into this booming field.

Access 61 lectures & 5 hours of content 24/7

Get a full introduction to Python Data Science

Get started w/ Jupyter notebooks for implementing data science techniques in Python

Learn about Tensorflow & Keras installation

Understand the workings of Pandas & Numpy

Cover the basics of the Tensorflow syntax & graphing environment and Keras syntax

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Complete Data Science Training with Python for Data Analysis

Learn Statistics, Visualization, Machine Learning & More

In this easy-to-understand, hands-on course, you'll learn the most valuable Python Data Science basics and techniques. You'll discover how to implement these methods using real data obtained from different sources and get familiar with packages like Numpy, Pandas, Matplotlib, and more. You'll even understand deep concepts like statistical modeling in Python's Statsmodels package and the difference between statistics and machine learning.

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Tensorflow Masterclass for Machine Learning & AI

This course is your complete guide to practical data science using the Tensorflow framework in Python. Here, you'll cover all the aspects of practical data science with Tensorflow, Google's powerful deep learning framework used by organizations everywhere.

Access 62 lectures & 5 hours of content 24/7

Get a full introduction to Python Data Science

Get started w/ Jupyter notebooks for implementing data science techniques in Python

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Machine Learning Terminology & Process For Beginners

Use Hands-On Labs to Learn Machine Learning Terminology & Processes

This course will help you learn machine learning terminology and processes with up-to-date knowledge. In this course, you'll learn and practice framing machine learning problems, data sets, data visualizations, evaluation, and more. You will also get complete resources and applicable codes in this course.

Access 26 lectures & 3 hours of content 24/7

Understand basic machine learning terminology & process

Learn how to frame a machine learning problem & when to use machine learning

Prepare & develop data sets

Instructor

Syed Raza has numerous technical IT and developer certifications (MCSE+I, MCT, CCNA—including a Ph.D. Management—which enable him to teach a variety of powerful courses, from IT to Project Management. He has been providing technical and training solutions using Microsoft Server 2016, 2019 Beta, Azure, Python, Java, JavaScript, React JS, GCP, Kubernetes, Docker. He has a working knowledge of TensorFlow, Pytorch, Keras, Convolutional networks, and data science concepts.